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ResearchCite this article: Esponda F, Gordon DM.

2015 Distributed nestmate recognition in ants.

Proc. R. Soc. B 282: 20142838.

http://dx.doi.org/10.1098/rspb.2014.2838

Received: 18 November 2014

Accepted: 5 March 2015

Subject Areas:behaviour, theoretical biology

Keywords:nestmate recognition, natural computing,

distributed systems, cuticular hydrocarbons

Author for correspondence:Deborah M. Gordon

e-mail: dmgordon@stanford.edu

Electronic supplementary material is available

at http://dx.doi.org/10.1098/rspb.2014.2838 or

via http://rspb.royalsocietypublishing.org.

& 2015 The Authors. Published by the Royal Society under the terms of the Creative Commons AttributionLicense http://creativecommons.org/licenses/by/4.0/, which permits unrestricted use, provided the originalauthor and source are credited.

Distributed nestmate recognition in ants

Fernando Esponda1 and Deborah M. Gordon2

1Department of Computer Science, Instituto Tecnologico Autonomo de Mexico, Mexico D.F. 01080, Mexico2Department of Biology, Stanford University, CA 94305, USA

We propose a distributed model of nestmate recognition, analogous to the

one used by the vertebrate immune system, in which colony response results

from the diverse reactions of many ants. The model describes how individ-

ual behaviour produces colony response to non-nestmates. No single ant

knows the odour identity of the colony. Instead, colony identity is defined

collectively by all the ants in the colony. Each ant responds to the odour

of other ants by reference to its own unique decision boundary, which is a

result of its experience of encounters with other ants. Each ant thus recog-

nizes a particular set of chemical profiles as being those of non-nestmates.

This model predicts, as experimental results have shown, that the outcome

of behavioural assays is likely to be variable, that it depends on the

number of ants tested, that response to non-nestmates changes over time

and that it changes in response to the experience of individual ants.

A distributed system allows a colony to identify non-nestmates without

requiring that all individuals have the same complete information and

helps to facilitate the tracking of changes in cuticular hydrocarbon profiles,

because only a subset of ants must respond to provide an adequate response.

1. IntroductionDistributed processes are widespread in nature as well as in engineered data

networks [1]. In systems without central control, the role of an individual

part depends on its interactions with other individuals. For example, in the

developing fly brain, lateral inhibition among neighbouring cells regulates

which cells eventually differentiate to become the sensory bristles [2]. Another

example is the vertebrate immune system, in which there is no single cell that is

able to identify every possible antigen; instead individual cells can each detect a

small subset [3]. Because an antigen is encountered by a variety of immune cells

when it enters the organism, there is a high probability that it will be identified,

thus conferring thorough coverage by the aggregate. The immune system can

track changes in ‘self’ efficiently because only a few immune cells must be

updated for every change. This provides a robust security system, difficult to

subvert since there is no single cell where all the information about ‘self’ is

kept and from which it can be stolen, hijacked or copied [4]. Methods for

data privacy and computer security [5–7], like the immune system [8], rely

on fragmenting and distributing sensitive information among a set of agents

so that each one holds only a piece of the puzzle. Their collective characteristics

confer adequate coverage for the host organism; together they define what is

acceptable by individually specifying what is not.

Distributed processes regulate not only the collective behaviour of cells, but

also that of groups of organisms [8]. Here, we propose a model of nestmate

recognition in social insects in which recognition depends on a distributed,

colony-wide process. Nestmate recognition has been observed in many species

of social insects [9], including wasps [10,11] and bees [12–15]. Our model

focuses on ants but may be broadly applicable to other taxa. Ants are an extre-

mely diverse and widespread taxon of more than 14 000 species. All ant species

live in colonies, and nestmate recognition is crucial in regulating colony cohe-

sion and interactions with other colonies. Ants are ecologically important in

every terrestrial ecosystem, and competition among colonies has important

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chemical profile 3

chemical profile 1

Figure 1. CHC-space for two chemicals. Each point represents a particularcombination of the chemicals 1 and 2.

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effects on ant population dynamics (e.g. [16]). In many

species, colonies engage in ongoing interactions with neigh-

bouring colonies that are crucial for the partitioning of

resources [17–19].

Many studies of nestmate recognition show considerable

variation among individuals in the extent of aggression (e.g.

[13,20,21]). Studies of nestmate recognition in ants rely on be-

havioural assays, with individuals from different colonies

placed in proximity, to determine when one ant identifies

another as belonging to a different colony. Recognition is con-

sidered to occur if individuals are more aggressive towards

non-nestmates than towards nestmates. The results of such

studies are notoriously variable (e.g. [20,22–24]). The out-

comes depend on whether the experiments were conducted

blind [24], on the worker task [23,25] and on the numbers of

ants included in the assay [20]. Often individuals of the same

colony differ in response (e.g. [26]).

The variability among individuals in their aggressive

response to individuals from other colonies has been treated

as a methodological problem rather than as a fundamental

feature of the process of nestmate recognition. The variability

is probably owing to many factors, such as variation among

individuals in their ability to detect chemical profiles [27]

and in differences among task groups in the likelihood that

they will respond in a bioassay [23]. However, current under-

standing of nestmate recognition is built on the premise that

variation among individuals of the same colony in response

to another colony is mostly owing to noise or undetected

influences of social context [28].

The model for nestmate recognition presented here

is based on a process in which individuals recognize

non-nestmates based on previous experience, with the

consequence that individuals within a colony differ in recog-

nition response. Nestmate recognition by the colony is

achieved through a distributed process, so that overall, ants

from one colony distinguish those from another, but individ-

uals do not always do so. We argue that variation makes the

system more effective and accounts for results that have until

now been taken to be owing to noise.

Our model of nestmate recognition begins with the pre-

mise that individuals acquire experience about nestmate

and non-nestmate odours over time. Ants learn odour cues

through exposure [26,29–32], over the course of hours

[33,34] and even as larvae [35], so that different ants may

react differently to the same chemical profile, and an individ-

ual ant may slowly change its reaction to a given odour.

Odour recognition in ants appears to be inclusive; ants

seem to be better at recognizing the presence of a new com-

pound rather than its absence [36,37]. Here, we consider

how nestmate recognition can emerge from the distribution

of various learned odours among ants within a colony.

2. Distributed detection modelNestmate recognition in social insects is olfactory. Hydrocar-

bons present on an insect’s cuticule (CHC) are sensed by

other workers and used to discriminate between nestmates

(individuals from the same colony) and non-nestmates

(those from other colonies). We describe the relation among

chemical profiles as in [38] with reference to cuticular hydro-

carbon or CHC-space, an n-dimensional Euclidean vector

space in which each coordinate axis represents the

concentration of a given hydrocarbon (figure 1). A particular

chemical profile is a point in this multi-dimensional space.

We assume that when an ant interacts with another one,

for example when two ants engage in antennal contact,

the focal ant can evaluate the absolute quantities of various

components in the other ant’s CHC profile [39] and esti-

mate the position of the other ant’s hydrocarbon profile in

CHC-space.

The notion of CHC-space has been used in many studies

to refer to the similarity between templates or profiles. For

example, the template similarity model [40] describes recog-

nition as a function of the distance between an individual’s

internal template, corresponding to the colony-specific CHC

profile of that individual’s colony, and the cues that individ-

ual detects from other individuals. In Newey’s [38] model,

recognition depends on the position, in CHC-space, of an

encountered odour relative to a region defined by the dis-

tance between the individual’s innate odour and that of its

colony. In the D-present and U-absent model [41], workers

use distances along axes, defined by specific desirable or

undesirable cues, to accept or reject individuals. In all of

these models, the colony odour is known to be a simplifica-

tion; individuals are known to differ slightly in CHC

profile. As Van Zweden et al. [42] point out, the results of

these models depend on how the colony odour is defined.

Here we propose that each ant carries, and can update,

the description of a boundary that partitions CHC-space

into two subsets: nestmate and non-nestmate. Each ant’s

boundary is the result of its individual history of encounters

with both nestmates and non-nestmates (figure 2). Because

ants differ in experience, each ant’s boundary is different,

and no individual ant’s boundary is the same as that of the

colony as a whole. Instead, the diverse positions of this

boundary for all ants intersect to create the colony’s profile

in CHC-space. This colony template in turn determines the

colony’s response to non-nestmates (figure 3). In the figures

we represent this boundary as a line to illustrate the idea as

simply as possible, but the shape of this boundary in the

many-dimensional CHC-space could itself vary among ants

within a colony, and is influenced by the mechanisms

involved in perception.

The figures were created using the logistic regression

model described in §2a; each boundary is initialized with a

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Figure 2. A single ant’s linear decision boundary in CHC-space. Any odour profileson the other side of the colony’s profiles are considered by the ant to be non-nestmates.

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random set of weights and habituated to a set of patterns

representing the colony’s profile.

We assume that each ant is consistent in its perception of

a chemical profile over the timescale under consideration,

and that the ant classifies all odours from a particular other

colony in the same way.

Let pi,j be the proportion of individuals from colony i that

recognize j as a non-nestmate chemical profile. Then

pi,i � 0, (2:1)

0 � pi,j , 1, i = j (2:2)

and P(Rij(n)) ¼ 1� (1� pi,j)n: (2:3)

Equation (2.1) states that nestmate profiles tend to elicit no

agonistic response, although in large colonies, when there

are many nestmates that never meet, it may be that occasion-

ally an ant recognizes a nestmate as a non-nestmate. This is

probably rare since it has almost never been observed.

Equation (2.2) states that each ant can recognize only a

subset of possible non-nestmate chemical profiles. Equation

(2.3) describes the probability of the recognition event R in

which an ant of colony j is recognized as a non-nestmate

by at least one out of n individuals from colony i, and

models our assumption that each ant recognizes a different

and independent subset of chemical profiles, so that the

colony’s nestmate recognition is distributed. Because individ-

uals tend to meet particular others of certain chemical

profiles, for example nestmates of the same task group,

who may tend to share chemical profiles [43], it is unlikely

that the boundaries of ants are completely independent, but

we use this assumption to simplify our model. Equation

(2.3) describes an equilibrium that occurs on the timescale

over which each ant is consistent in its perception of a chemi-

cal profile and consistently recognizes a unique subset of

non-nestmate odours.

Finally, as a corollary of equation (2.3), the probability

that a recognition event occurs between n ants of colony iand m ants of colony j is

P(Rij(n) or R ji(m)) ¼ P(Rij(n))P(R ji(m))

þ P(Rij(n))(1� P(R ji(m)))

þ (1� P(Rij(n)))P(R ji(m)),

(2:4)

where the last two terms refer to unilateral recognition.

In the following, we use aggression as a proxy for recog-

nition, since aggression implies recognition (although we

recognize that the reverse is not true). The probability of

aggression is a fraction of the probability of R

P(Aij(n, m)) ¼ a1P(Rij(n))P(R ji(m))

þ a2P(Rij(n))(1� P(R ji(m)))

þ a3(1� P(Rij(n)))P(R ji(m)),

(2:5)

where the ais establish the probability of aggression for

bilateral and unilateral recognition.

We now consider how an ant’s boundary between

nestmates and non-nestmates in CHC-space might shift

over time. Such shifts would lead to continual changes in

the colony’s response to non-nestmates.

(a) Shift in individual recognitionAn ant’s boundary between nestmates and non-nestmates in

CHC-space can be understood as the chemical combinations

for which there are sufficient neural activity to trigger percep-

tion of a non-nestmate. We suggest that boundaries shift as a

result of a process similar to the one reported in studies

such as those of Guerrieri et al. [44] and van Zweden &

d’Ettorre [9], in which response to nestmate odours is

reduced by exposure to nestmates. The boundary is estab-

lished by habituation, and continually modified by

experience, for example by repeated encounters with an

unfriendly conspecific non-nestmate.

As an example we describe how CHC profiles could be

learned using the collective action of a metaphorical set of

neurons referred to as the ‘recognizer’, modelled as a logistic

regression [45]. Each recognizer receives the information cor-

responding to m inputs or cues and has a weight associated

with each one that determines its importance. The recognizer

fires, producing an output according to: output(x) ¼1=(1þ e�wTx), where x is the input pattern vector whose com-

ponents are individual hydrocarbons, and w is a weight

vector of real numbers that express the positive or negative

role of each hydrocarbon. The output function makes rapid

and continuous transitions between 0 and 1. We model

each ant as having an internal threshold beyond which

recognition of a non-nestmate takes place:

recognize(x) ¼ Yes; if output(x) � thresholdNo; otherwise:

�

The exact value for the threshold is not important,

so without loss of generality, assuming a threshold ¼ 0.5,

the boundary that separates recognition as non-nestmate

from recognition as nestmate is when (1=(1þ e0)) ¼(1=(1þ 1)) ¼ 0:5, which occurs when wT x* ¼ 0. These are

the values for x plotted in figures 2 and 3. Each ant’s separ-

ating boundary between nestmates and non-nestmates

arises from its particular set of weights w.

Learning in this context is the process that finds suitable

values for w. Diversity among ants arises because ants

differ in weights. We assume that over successive encounters,

ants habituate to an odour if each encounter with the odour is

not associated with aggression, or if there is a positive experi-

ence such as trophallaxis or grooming [9,23,34,44,46], while

aggression or the absence of a positive experience leads to

gradual sensitization to non-nestmate odours. This assump-

tion is supported by previous studies. Knaden et al. [47]

and Van Wilgenburg et al. [48] found that in successive

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Figure 3. Overall colony recognition. Each line represents a boundary in CHC-space for one individual. The region of odour profiles that will be accepted by thecolony, corresponding to the region on the nestmate side of the boundary for all individuals, is located in the small open area in the lower, central part of the figure,at about coordinates [0.58, 0.10]. The figures are based on the logistic regression model and habituation algorithm of §2a. Panels (a) 100 and (b) 500 separatingboundaries.

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encounters between non-nestmates, aggression increased.

An exception to this was reported by Nowbahari [49], who

found that ants of Cataglyphis niger reduced aggression

towards particular individual non-nestmates in successive

encounters, suggesting that an ant may habituate to the

odour of a particular non-nestmate. However, in succes-

sive encounters with different non-nestmate individuals,

aggression did not decrease.

Weights could be adjusted by a procedure akin to the

Delta rule [50] whereby small changes to the positive and

negative weights are made in the appropriate direction in

response to each encounter. In this way, a simple mechanism,

providing each ant with a crude characterization of chemical

profiles, can collectively distinguish nestmates and non-

nestmates (figure 3) for the colony as a whole. Although

ants probably use much richer neural processes to perform

this task [33,51], a simple mechanism is sufficient to model

recognition behaviour.

3. Discussion(a) Fit with existing dataIn this section, we consider how results from the literature sup-

port our model. We focus primarily on studies that track the

response of individual workers towards non-nestmates in

repeated encounters.

Experiments by Newey et al. [26] on weaver ants

(Oecophylla smaragdina L.) show that in repeated exposures,

an individual ant reacted similarly to similar chemical pro-

files, but different ants from the same colony responded

differently to the same chemical profile. A further exper-

iment, in which an individual from the recipient colony

met workers from different colonies, showed that a particular

ant reacts differently to different odours. These findings sup-

port our assumptions that workers have a boundary that

consistently separates the same nestmate and non-nestmate

odours, that not all non-nestmate odours are perceived as

such by a given individual, and that individual workers

within a colony differ in the boundary that separates

nestmate from non-nestmate chemical profiles.

Newey et al. [26] suggest that there is a set of diverse tem-

plates based on individual odours and that recognition

requires both an individual and a colony profile, with aggres-

sive response as a function of the distance between the two

[38]. Their explanation is that each ant has encoded a different

tolerance area in CHC-space defined by both the individual’s

innate odour and the colony odour. In our model, by con-

trast, the region of rejection for an individual is not

bounded by its own odour; thus, although individuals

differ in profile, an ant with a profile closer to that of the

colony may exhibit the same aggressive behaviour as an ant

whose profile is further from that of the colony. This occurred

in a study of harvester ants (Pogonomyrmex barbatus) [23];

other studies are needed to determine whether this occurs

in many ant species and under what ecological conditions.

Roulston et al. [20] investigated how aggressive behaviour

between non-nestmates depends on the number of ants in each

trial. Their experiments report the proportion of trials that have

at least 1 aggressive encounter in assays with 1 versus 1, 1

versus 25, and 5 versus 5 live ants, as well as an assay with

1 live versus 1 dead ant. In a distributed model such as ours

and Newey’s [38], different ants identify a different subset of

chemical profiles so, for assays with few ants (1 versus 1),

we expect high variability among replicates as was observed,

and the average over many replicates to approximate the

real frequency of aggression in 1 versus 1 encounters. The dis-

tributed model would also explain why a more consistent

aggression score was observed in the 5 versus 5 assays, since

it includes several 1 versus 1 interactions.

Our model predicts that the probability of aggression in

encounters between non-nestmates depends more on the

number of different non-nestmates that each ant encounters,

than on the total number of ants or total number of inter-

actions. For example, aggression is more likely when 5 ants

of colony A meet 5 ants of colony B, than when 1 ant of

colony A meets 25 ants of colony B. In the first case, there

are 5 ants that might recognize B as a non-nestmate, while

in the second case, only 1 ant may recognize B, and if it

does not recognize one B it is unlikely to recognize the other

24. We tested this quantitatively using the data from [20]

and equation (2.3), which establishes the probability that a

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foreign ant is recognized as a function of the number of

encounters, and equation (2.5) with a1 ¼ 1 and a2 ¼ a3 ¼ 0.

We assume only two-sided recognition because there was

less aggression in the live versus dead than live versus live

assays in Roulston et al. [20], and other work [23] suggests

that aggression is more likely when both participants recog-

nize the other as non-nestmates. This produces numbers

lower than the observed experimental averages, because one-

sided aggression does sometimes occur. We approximate pi,j,,the proportion of ants of colony i that recognize the colony

profiles of colony j as that of non-nestmates in each exper-

iment, using equation (2.5) by taking the square root of the

observed proportion of aggressive encounters in the 1 versus

1 assays for each pairing. We thus take p2i,j to reflect the prob-

ability of an aggressive act between two ants. Our predictions,

along with the results of Roulston et al. [20], are shown in elec-

tronic supplementary material, table S1. Our model yields a

good approximation for the observed ordering in the fre-

quency of aggression for the 1 versus 1, 1 versus 25 and 5

versus 5 experiments.

Although there were an equal number of possible encoun-

ters in the 1 versus 25 experiments and in the 5 versus 5

assays, the level of aggression was lower (electronic sup-

plementary material, table S1), as expected, since we

assume that a particular individual classifies all odours

from the same colony in the same way: in terms of equation

(2.3), P(Ri,j(1)) ¼ 1 2 (1 2 pi,j) ¼ pi. Conversely, each of the

1 ant’s 25 adversaries has an independent chance of recogniz-

ing the single ant; using equation (2.3), P(Rj,i(25)) ¼ 1 2 (1 2

pj,i)25. Thus the probability of observing an aggressive

encounter in the 1 versus 25 assay using equation (2.5) is

P(Ai,j(1,25)) ¼ P(Ri,j(1)) P(Rj,i(25)). In the 5 versus 5 assays,

each single ant classifies all the odours from its adversary

colony in the same way, but in contrast with the 1 versus

25 assay, both colonies have many (five) individuals in

the match. Using equation (2.3), P(Rj,i(5)) ¼ P(Ri,j(5)) ¼ 1 2

(1 2 pj,i)5 so the probability of aggression is P(Ai,j(5,5)) ¼

P(Ri,j(5)) P(Rj,i(5)). Because we assume that a single ant is con-

sistent in its classification of a colony’s odours and each ant

recognizes as foreign a distinct subset of chemical profiles,

for a given value of pi,j, P(Aij(1,1)) � P(Aij(1,25)) �P(Aij(5,5)). This result is consistent with the observations

(electronic supplementary material, table S1). There were

exceptions in two trials, which our model does not account

for: in FORb-FORs and EMI-GRF.

Next we consider how the results in Van Wilgenburg et al.[48] support our model’s assumption that ants show consist-

ent differences in which chemical profiles they recognize.

Pairs of ants from different colonies were put together, and

later, one ant from each pairing, the focal ant, was placed

with another ant of the same colony as the non-nestmate

from the previous trial. About 52% of the initial assays

resulted in aggressive behaviour, and 78% of the focal ants

showed the same behaviour, aggressive or not, during their

second encounter with a different non-nestmate of the same

colony. Results did not differ when the focal ants were

presented with non-nestmates of other colonies; however,

non-nestmate CHC profiles may have converged in response

to a similar laboratory diet [44]. These results suggest that

ants of one colony differ consistently in their sensitivity to

particular non-nestmate CHC profiles.

During the second encounter, the percentages of trials

with aggressive behaviour for the initially passive and

initially aggressive ants were 29% and 89%, respectively,

leading to an overall increase in aggressive behaviour from

52% in the first round of encounters to 60% in the second

round. There are several alternative explanations for the

observed increase. If recognition of a non-nestmate odour is

random, so that an individual is equally likely to recognize

a given odour as nestmate or non-nestmate in consecutive

encounters, then the expected distribution of passive and

aggressive ants during the second round would be the

same as in the first round. According to this explanation,

the observed distributions in [48] would have to be owing

to two distinct unexplained shifts: a moderate (19%) dimin-

ution in aggression for the initially passive ants, from 48 to

29%, and a large (37%) increase in aggression for the initially

aggressive, from 52 to 89%.

Another possibility is that the passive ants remain passive

and the aggressive ants remain aggressive, leading to 0%

aggression for the initially passive ants and 100% aggression

for the initially aggressive set. Then the observed distri-

butions would have to be produced by two unexplained

shifts: a large increase in aggression for the initially passive

ants (29%) and a moderate decrease in aggression (11%) for

those that were initially aggressive. This interpretation is

consistent with the authors’ hypothesis that exposure to an

adversary, even in the absence of a fight, increases the

likelihood of subsequent aggression.

An alternative explanation provided by our model is that

the observed distributions are owing only to one shift: a mod-

erate increase in the aggression (17%) of the initially aggressive

ants. In our model, each ant is consistent, during a short time

period, in recognizing particular odours as those of non-

nestmates, and again we assume that aggression is more

likely between two non-nestmates if both individuals recog-

nize each other as non-nestmates than if only one recognizes

the other as a non-nestmate (as in [23]). When we then calcu-

late the expected distributions of passive and aggressive ants

during the second round, assuming no overall increase in

aggression (electronic supplementary material, appendix S2),

we obtain 30% and 72% of aggressive encounters for the

initially passive and initially aggressive ants, respectively, in

contrast to the observed 29% and 89%. We suggest that the

observed increase in aggression is primarily explained by a

heightened propensity for aggression, even in the absence of

bilateral recognition, for the initially aggressive ants (as

reported by Hsu et al. [52] in other taxa). In contrast to the

experiments in [49], which showed a diminution of aggression

between particular ants in successive encounters, ants in this

study [48] did not face the same adversary in the second

round. Thus, our model provides a simple explanation for

the observed results that requires only a single process. Further

experiments are needed to test this explanation.

Guerrieri et al. [44] demonstrate that the addition of

certain components to an ant’s CHC profile make it sus-

ceptible to aggression by its untreated nestmates. This is

consistent with our predictions. Treated ants were left

together to acclimate, providing opportunities for repeated

encounters, before the bioassays were performed. Aggression

by a group of untreated ants towards a treated one was

higher than that by a group of treated ants towards an

untreated one. Our model suggests that the boundaries of

the treated ants adjusted to include the added compound,

so that treated ants were likely to recognize the untreated

ants as nestmates, because the odour of the untreated ants

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was included in the treatment mixture. However, an

untreated ant would detect as foreign the presence of an

added compound if its decision boundary were close to

that of the colony’s odours. Such asymmetry in recognition

was reported by Bos et al. [36], and the mechanism they

propose might account for the results of Guerrieri et al. [44].

In another study, Sturgis & Gordon [23] demonstrated that

the probability of aggression depends on task group: exterior

workers are significantly more aggressive than interior

workers, and recognition is more likely towards ants of

neighbouring colonies [29,53], which are often encountered

by exterior workers [18], than ants of more distant colonies.

These results suggest that aggression is related to the past

history of encounters: individuals that have previously met

non-nestmates are more likely to recognize them [47,48].

Honeybee guards outside the hive react towards

intruders that are non-nestmates. However, recognition of

non-nestmates by bees tends to be inaccurate, with a mean

rejection rate of non-nestmates of only 51% (mean based on

Johnson et al. [21]). This is consistent with our model:

individual bees vary in the boundary in CHC-space that

separates the odours of nestmates and non-nestmates and

that recognition is distributed. Other studies show that in

the short-term, individuals are consistent in which odours

they recognize as non-nestmates [12,54], and habituation to

conspecific odours has been demonstrated in several studies

[54–56] suggesting that a bee’s recognition ability changes

as a result of its interactions with other bees.

Our model may help to explain the apparently contradic-

tory results on neighbour recognition in ants. Some studies

show that ant colonies distinguish the odour of neighbouring

colonies [29,30,57]; for example, encounters with neighbouring

foragers inhibit foraging more than encounters with ants from

distant ones. This is puzzling because field and laboratory

studies show that it is unlikely for any individual ant to

meet others of the same neighbouring colony many times

[58,59]. We suggest this effect may be owing to small shifts

in the recognition boundary of ants who have met a non-

nestmate only a few times. Consider two neighbouring

colonies, A and B, and a third colony C, so distant that ants

from A and B never encounter ants of C. Every encounter

that an ant from A has with an ant from B shifts that A ant’s

boundary slightly. However, repeated encounters between A

and B do not systematically affect the relationship between

A and C, whose ants never meet. In a test such as the one in

[29], in which large numbers of ants from A can encounter

some from B, some ants from A are likely to respond to B,

because some of them have previously met an ant from B, but

none are as likely to respond to C, because none of the A ants

have ever met any C ants. Whether such recognition results in

aggression or a ‘dear enemy’ response [30,57] may depend on

the particular ecological conditions that determine the extent

of competition and costs of aggression between neighbours.

(b) Predictions and conclusionsWe propose a distributed model of nestmate recognition that

predicts the variability in ant aggressive behaviour that is

consistently observed in experimental assays. Our model dif-

fers from other models of nestmate recognition in describing

recognition as a function of the position of a detected odour

with respect to a boundary surface in CHC-space, rather than

as a function of the distance between odours (figure 2). Our

proposal integrates features of other models: it supports

Guerrieri et al.’s [44] conclusion that ants recognize non-

nestmates rather than nestmates, and generalizes Newey’s

[38] proposal by allowing arbitrary shapes for the colony

decision boundary in CHC-space that are not bound by the

innate odours of particular individuals. Our model also

extends the D-present, U-absent [41] and U-present [9,44]

mechanisms by modelling an individual as perceiving a

chemical bouquet in which some cues can be excitatory and

some inhibitory. In contrast to previous models, in our

model each ant may be sensitive to different cues, for

example by detecting odour as a linear combination of chemi-

cals containing both positively and negatively weighted cues.

Thus, our model posits a malleable decision boundary for the

colony that emerges from the aggregated experiences of each

individual. The decision boundary could arise from a variety

of mechanisms, such as the pre-filter [60,61] or neural

template [34,62] mechanisms, which may vary among species.

We suggest that each ant in a colony learns to recognize a

unique set of non-nestmate odours, so that ants are likely to

agree more in accepting nestmates than in aggression

towards non-nestmates. Our model is similar to that of

Johnson et al. [21], in that each ant has a limited recognition

capability, and recognition of non-nestmates is the result of

the collective reaction of the colony, but differs strongly

from theirs in its assumptions and thus in its predictions

(see [63] for comments on this model). In Johnson et al.’s[21] simulation, individual recognition ability is random,

while in ours, recognition ability is a consequence of prior

experience [26,29–32,44] and thus consistent in response to

specific odours at any given time. We suggest that ants

hold a neurologically encoded decision rule that takes the

form of a separating surface in CHC-space (§2a). Thus iden-

tity is not inscribed in a template commonly shared by all

ants in a nest; it is partially encoded in each of its members

and distributed among them (figure 3).

Further work is needed to test the predictions of our

model. First, it predicts that individuals that initially do not

respond aggressively towards a particular odour can develop

an aggressive response towards that odour if it is associated

with negative experience such as aggression. This has been

found in several studies, including Knaden & Wehner [47]

and Van Wilgenburg et al. [48]. More tests of this are needed.

Second, we predict that the more an individual is exposed

to non-nestmates, the greater the precision of its discrimi-

nation of non-nestmates. In general, aggressive behaviour

should be more likely, the more non-nestmates an ant has

encountered. Repeated encounters with diverse odours will

tend to position the boundary closer, on average, to the set

of odours of its nestmates, i.e. to the colony profile. The

closer an ant’s boundary to the colony profile, the broader

the range of non-nestmate chemical profiles that it will be

able to recognize, and the more accurately it will be able to

identify foreign odours. This can be tested with experiments

involving repeated encounters, such as those in Van

Wilgenburg et al. [48], to test whether, as an ant is exposed

to more other ants, it can identify as non-nestmates ants

whose chemical profile is more similar to its own because

its boundary between nestmates and non-nestmates moves

closer to its own colony’s profile (figure 3). This prediction

is supported by the widespread observation that recognition

mistakes in the form of aggression between nestmates are

extremely rare, while mistakes in the form of a lack of

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aggression, or failure to recognize non-nestmates, are extre-

mely common. It appears that the initial response, or

default response of a young ant, is lack of aggression. A

bias towards lack of aggression towards nestmates, because

the vast majority of a worker’s encounters are with nestmates,

favours colony cohesion at the risk of allowing intrusion or

other harm from non-nestmates. Younger ants tend to work

inside the nest [64], where they are likely to meet only

nestmates. The more similar are the odours of nestmates,

the more likely they are to be on the same side of the bound-

ary. Only later, as they work outside the nest, e.g. as foragers,

are they likely to meet ants of other colonies. Thus, as some

studies show [23], ants that leave the nest, such as foragers,

should be more likely to respond aggressively to non-

nestmates than ants that work only inside the nest, such as

brood care workers.

Third, we predict that although in general, the distances

between chemical profiles in CHC-space should be correlated

with the probability of aggression [9], recognition is not a

simple function of the distance between an individual’s

innate odour and the colony’s profile. Instead it is a function

of the distance between an ant’s decision boundary and the

intruder’s expressed odour. Thus, two odours may be very

close to each other but on distinct sides of the decision

boundary, while another two may be far from each other

yet classified together, depending on where they lie relative

to the ant’s boundary.

This implies that the relationship between the probability

of recognition and the distance between profiles in CHC-

space should change as a result of individual experience, as

shown in [36,44,65]. Our model predicts, in contrast to the

two-odour model [38], that an individual could accept, as a

nestmate, another one with a profile that is actually further

than its own from the overall colony’s profiles. Studies con-

sistent with this prediction include those of Fielde [66],

with colonies formed of mixed species combined after eclo-

sion, and the recent result that in harvester ants, CHC

profiles of some task groups were more similar within task

group, across colonies, than within colonies [23]. Further

studies such as those of [46] that measure individual odour

in isolated ants are needed to investigate this.

Evidence against our model would come from studies

that establish a lack of variation among individuals in

response to odours. An example would be the observation

that every ant in a colony identifies the same set of non-

nestmates, and that they vary only in their amount of aggres-

sion. This could be addressed using a neurophysiological

measure, rather than aggression, to determine whether recog-

nition occurs. Other evidence against our model would be the

demonstration that aggression is random with respect to

odour, without any effect of previous exposure. However,

there seems to be substantial evidence that this is not the case.

Using a distributed model of nestmate recognition

allows colonies to take advantage of large numbers of indi-

viduals to devote fewer resources to the identification of

non-nestmates. Colony hydrocarbon profiles change over

time [67,68]. A distributed system facilitates tracking of

these changes, because only a subset of ants must respond

to the change to provide an adequate response. The apparent

variability in experimental results in studies of recognition by

ants reflects not noise, but an effective method for maintain-

ing colony security. As in distributed systems elsewhere in

nature, such as in the immune system, distributed detection

allows colonies to adapt to slow changes in colony odours,

allows ants to share the energetic burden of detection and

provides the colony with a robust security system. The

system favours lack of aggression towards nestmates, risking

possible errors in the recognition of non-nestmates, in the

same way that the immune system favours the inhibition of

autoimmune responses while risking infection. A distributed

process for recognition may be important in providing group

identity to aggregates in many natural systems.

Acknowledgements. We thank Michael J. Greene, Mark Brown, Nick Bosand anonymous reviewers for very helpful comments on themanuscript.

Funding statement. We gratefully acknowledge the support provided byAsociacion Mexicana de Cultura, A.C. to F.E. and by the CiscoResearch Center University Funding program to D.M.G.

Author contributions. Both authors developed the model and wrote themanuscript. Both authors gave final approval for publication.

Conflict of interests. The authors have no competing interests.

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